Serotonin Transporter Gene (SLC6A4) Variations Are Associated with Poor Survival in Colorectal Cancer Patients
Bibliographic record
Abstract
Prognosis in colorectal cancer patients is quite variable, even after adjustment for clinical parameters such as disease stage and microsatellite instability status. It is possible that the psychological distress experienced by patients, including anxiety and depression, may be correlated with poor prognosis. In the present study, we hypothesize that genetic variations within three genes biologically linked to the stress response, namely serotonin transporter (SLC6A4), brain-derived neurotrophic factor (BDNF), and arginine vasopressin receptor (AVPR1B) genes are associated with prognosis in colorectal cancer patients. We used a population-based cohort of 280 patients who were followed for up to 12.5 years after diagnosis. Our multivariate analysis showed that a tagSNP in the SLC6A4 gene (rs12150214) was a predictor of shorter overall survival (HR: 1.572, 95%CI: 1.142-2.164, p = 0.005) independent of stage, age, grade and MSI status. Additionally, a multivariate analysis using the combined genotypes of three polymorphisms in this gene demonstrated that the presence of any of the minor alleles at these polymorphic loci was an independent predictor of both shorter overall survival (HR: 1.631, 95%CI: 1.190-2.236, p = 0.002) and shorter disease specific survival (HR: 1.691, 95%CI: 1.138-2.512, p = 0.009). The 5-HTT protein coded by the SLC6A4 gene has also been implicated in inflammation. While our results remain to be replicated in other patient cohorts, we suggest that the genetic variations in the SLC6A4 gene contribute to poor survival in colorectal cancer patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".